arXiv subjects

Francesco Crescenzo Grasso

Publications and source records attributed to Francesco Crescenzo Grasso.

2 recordsLinked to original sources

arXiv2026

VulDec: Vulnerability Detection in Binaries via Neural Decompilation

Automated vulnerability detection in binary executables is challenging due to the lack of source code and the limited semantic information available at the assembly level. Despite significant progress in machine-learning-based code analysis, the field still lacks methodologies and large, high-quality datasets that provide vulnerable code jointly at assembly- and source-level with precise vulnerability labels. To address this gap, we introduce VulDec, consisting of (i) a new large-scale, carefully curated dataset aligning real-world Assembly-C function pairs with detailed CWE annotations across multiple architectures, and (ii) the first end-to-end framework that leverages neural decompilation for binary vulnerability detection. Tuned LLM-based neural decompilers trained on our dataset achieve strong source-level recovery across architectures (Edit Similarity of approximately 56%, BLEU-4 of approximately 43%), while our tuned LLM-based detectors reach a macro F1-score of 0.61 on an in-distribution realistic test set (3% vulnerability rate). Our findings provide evidence that neural decompilation offers a powerful alternative to conventional low-level binary representations, facilitating robust binary vulnerability detection.

cs.CR↗

arXiv2026

Enhancing LLM-Based Vulnerability Detection via Representation-Aware Logical Ensemble

Automated vulnerability detection in C source code is a long-standing challenge, as traditional static analysis tools often trade precision for scalability and struggle with complex vulnerability patterns. Recently, large language models (LLMs) have shown promising results; yet, most approaches rely on a single code representation, implicitly assuming uniform suitability across vulnerability types. In this work, we systematically study LLM-based vulnerability detection under multiple code representations, including raw source code, Abstract Syntax Trees (AST), and Control Flow Graphs (CFG). Through extensive empirical analysis, we show that different representations exhibit complementary strengths, with specific views being more effective for particular classes of vulnerabilities. Building on these findings, we propose a Representation-Aware Logical Ensemble (RALE) that integrates LLMs trained on different code representations through explicit logical rules and majority voting. While single-representation models achieve up to 73% F1-score, our integrated approach reaches 78% F1-score, with commensurate gains in accuracy, precision, and recall, consistently outperforming individual models. These results highlight the importance of explicitly leveraging complementary code representations when combining LLMs for effective vulnerability detection.

cs.CR↗